[Model][3/N] Automatic conversion of CrossEncoding model

vllm/model_executor/models/gemma.py

@@ -43,6 +43,7 @@
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
+from .adapters import as_seq_cls_model
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
@@ -425,3 +426,7 @@ def load_weights(self, weights: Iterable[tuple[str,
                            if self.config.tie_word_embeddings else None),
         )
         return loader.load_weights(weights)
+
+
+class GemmaForSequenceClassification(as_seq_cls_model(GemmaForCausalLM)):
+    pass

vllm/model_executor/models/registry.py

@@ -174,6 +174,8 @@
                                             "RobertaForSequenceClassification"),
     "ModernBertForSequenceClassification": ("modernbert",
                                             "ModernBertForSequenceClassification"),
+    # [Auto-converted (see adapters.py)]
+    "GemmaForSequenceClassification": ("gemma", "GemmaForSequenceClassification"), # noqa: E501
     "Qwen3ForSequenceClassification": ("qwen3", "Qwen3ForSequenceClassification"), # noqa: E501
 }